Modern Physics Letters B 2050408 (24 pages) © World Scientific Publishing Company DOI: 10.1142/S0217984920504084 Recent trends on community detection algorithms: A survey Sumit Gupta * and Dhirendra Pratap Singh Department of Computer Science & Engineering, Maulana Azad National Institute of Technology, Bhopal, Madhya Pradesh, India * sumitgupta888@gmail.com Received 1 March 2020 Revised 1 June 2020 Accepted 7 June 2020 Published 17 September 2020 In today’s world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate; applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. Community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories; non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected. Keywords : Community detection; social network; graph partitioning; graph cluster- ing; overlapping community detection algorithm; non-overlapping community detection algorithm. 1. Introduction When the real-world application data are represented with the help of graphs, nodes represent the person or object of the application. In contrast, links or edges represent the relationship among the nodes. These graphs contain large numbers * Corresponding author. 2050408-1 Mod. Phys. Lett. B Downloaded from www.worldscientific.com by UNIVERSITY OF NEW ENGLAND on 09/21/20. Re-use and distribution is strictly not permitted, except for Open Access articles.